Adaptation of field use spectroscopy equipment

ABSTRACT

A spectrometer configurable for field analyses of chemical properties of a material is provided. The spectrometer includes: at least one sensor adapted for providing Fourier transform infrared spectroscopy (FTIR) surveillance and at least another sensor for providing Raman spectroscopy surveillance. The spectrometer can be provided with a user accessible instruction set for modifying a sampling configuration of the spectrometer. A method of determining the most likely composition of a sample by at least two technologies using the spectrometer is also provided.

CLAIM OF PRIORITY

This application claims the benefit of U.S. provisional patentapplication No. 61/920,230, filed Dec. 23, 2013. The contents of thisapplication are incorporated by reference in their entirety.

GOVERNMENT SUPPORT

This invention was made with Government support under Contract NumberN00174-13-C-0032 awarded by Naval Explosive Ordnance Disposal TechnologyDivision (NAVEODTECHDIV). The Government has certain rights in theinvention.

BACKGROUND

1. Field of the Invention

The invention relates to field use spectroscopy equipment, and inparticular, to adaptation of radiological and/or optical detectionand/or identification equipment.

2. Description of the Related Art

A variety of instruments are used in the field to assist with hazardassessment and control. When users, whether it be a first responder orsoldier, are called to respond to a situation, they frequently have someintelligence about the situation. In the case of a first responder, theyhave information from a call to emergency services where somebody mayhave explained what happened. In the case of a soldier, the infantryscouting a territory will identify something suspect and call in theappropriate personnel. When these users arrive on scene they frequentlyare under time constraints to prepare their tools and respond. In thecase of the soldier, they may be getting shot at. In the case of thefirst responder, there may be victims that need rescuing or pressure toreopen an evacuated building. Making proper assessments with suchinstruments is only complicated by the increasing complexity of thesetools. Accordingly, whether the responder is under a time constraint, oris not adequately familiar with the instrument, setting the variousparameters can be a challenge.

Some of these instruments allow for configuration of the instrument tooptimize performance. This optimization may be to have the instrumentdetect/identify or alarm on a specific kind of chemical, or list ofchemicals. The optimization may be to improve performance, user safetyor to have the instrument perform in accordance with the standardoperating procedures (SOP). Current instruments that allow for userconfiguration offer a manual process that permits editing the settingsone at a time. Manual configuration takes time, while the user is undertime constraints, and therefore many times the instruments are notconfigured for optimal performance. This puts the users at risk andslows down the response.

What are needed are methods and apparatus for adapting or reconfiguringfield instruments. Preferably, the solutions make use of fieldintelligence to assist a user with configuration control. Additionally,the solutions should be expedient and provide for improved performanceof the instrumentation.

In addition, there is an ever increasing need for field-portableanalyzers capable of reliably identifying unknown materials. Emergencyresponse teams and law enforcement agencies frequently encounter unknownand potentially hazardous substances including toxic industrialchemicals (TICs), narcotics, explosives precursors, and improvisedexplosive devices (LEDs). In addition to these substances, conventionalexplosives, biological weapons, and chemical weapons continue to bethreats for homeland security and military users. In the laboratoryenvironment, mid-infrared and Raman spectroscopy have proven veryeffective for identifying such materials. Efforts to transitionvibrational spectroscopy from a laboratory analytical technique to afield based tool have been on-going for more than a decade, and inrecent years handheld spectrometers have been widely successful in anumber of applications.

Field users of handheld spectrometers typically do not have extensivetraining in science or spectroscopy. As such, an important designconsideration for such devices is to incorporate on-board intelligencecapable of converting raw spectral data into answers. In qualitativeapplications, the question being asked by the end user frequently fallsinto one of three categories:

1) authentication: Is the measured test material consistent with genuinesubstance X?

2) screening: Does the measured test material appear to containsubstance X?

3) identification: What material was measured?

The problem associated with authentication is quite bounded (e.g., “Isthe measured spectrum consistent with a stored reference spectrum ofmaterial X?”). Authentication algorithms are typically used for rawmaterial confirmation and anti-counterfeiting applications, and will notbe considered further herein.

Screening algorithms evaluate whether at least a subset of features inan unknown measurement correspond to one or more specific substances ofinterest. Such algorithms require user input regarding the potentialpresence of materials (e.g., what test targets are being searched for,and what interferents are likely to be encountered). Thus, the screeningapplication is also bounded, albeit not to the extent thatauthentication is. As such, screening algorithms are most attractive forscenarios where the instrument operator has knowledge regarding thepotential presence of specific analytes.

Identification, or library searching, algorithms are configured to scoura library of known materials and determine whether the unknown spectrumis consistent with any stored responses from the database. Whilelower-end devices stop with pure material assessment, more sophisticatedidentification equipment incorporates automatic mixture analysis that isinvoked if the unknown measurement does not match any library spectra.The mixture analysis is performed to determine whether a combination ofstored responses explaining a significant portion of the measured datacan be found. This is of great practical utility as samples encounteredin the field are frequently impure. Identification algorithms are veryflexible in the sense that they can identify an unknown material frommany thousands of possible candidates; however, they do not incorporateinformation regarding the potential presence of specific analytes thesame way that screening algorithms do. Thus, screening algorithms oftenprovide enhanced detection capability which makes them attractive forspecific applications such as chemical warfare agent or narcoticsdetection.

Portable analytical devices based on a range of technologies, such asinfrared spectroscopy, Raman spectroscopy, X-ray fluorescencespectroscopy, mass spectrometry, etc. are now widely available anddeployed globally. However, there is a continuing need for aspectrometer that combines a sensor adapted for providing Fouriertransform infrared spectroscopy (FTIR) surveillance and a sensor forproviding Raman spectroscopy surveillance.

SUMMARY

In one embodiment, a hand-held spectrometer configurable for fieldanalyses of chemical properties of a material is provided. Thespectrometer includes: at least one sensor adapted for providing Fouriertransform infrared spectroscopy (FTIR) surveillance and at least anothersensor for providing Raman spectroscopy surveillance. In someembodiments, the spectrometer can be provided with a user accessibleinstruction set for modifying a sampling configuration of thespectrometer. In certain embodiments, the spectrometer can be providedwith a user accessible instruction set to configure the spectrometer. Insome embodiments, the spectrometer can be provided with a plurality ofuser accessible response profiles, each response profile providing aninstruction set for modifying a sampling configuration of thespectrometer.

In another embodiment, a method of determining the most likelycomposition of a sample by at least two technologies using aspectrometer includes obtaining data from the sample by a firsttechnology using the spectrometer, wherein the data comprises a firstrepresentation of a measured spectrum obtained by the first technology,and determining a precision state of the first representation of themeasured spectrum. The method further includes providing a first set oflibrary candidates and, for each library candidate, providing datarepresenting each library candidate, wherein the data comprises arepresentation of a library spectrum obtained by the first technology.The method then further includes selecting a first subset of librarycandidates by determining a first representation of the similarity ofthe sample to each library candidate in the first set of librarycandidates using (i) the first representation of the measured spectrum,(ii) the precision state of the first representation of the measuredspectrum, (iii) the representation of the library spectrum for thatlibrary candidate, and, optionally, (iv) the precision state of therepresentation of the library spectrum for that library candidate. Themethod then includes determining a first most likely composition of thesample based upon the selected first subset of library candidates.

The method further includes obtaining data from the sample by a secondtechnology using the spectrometer, wherein the data comprises a secondrepresentation of a measured spectrum obtained by the second technologyand determining a precision state of the second representation of themeasured spectrum, providing a second set of library candidates and, foreach library candidate, providing data representing each librarycandidate, wherein the data comprises a representation of a libraryspectrum obtained by the second technology. The method then furtherincludes selecting a second subset of library candidates by determininga second representation of the similarity of the sample to each librarycandidate in the second set of library candidates using (i) the secondrepresentation of the measured spectrum, (ii) the precision state of thesecond representation of the measured spectrum, (iii) the representationof the library spectrum for that library candidate, and, optionally,(iv) the precision state of the representation of the library spectrumfor that library candidate. The method then includes determining asecond most likely composition of the sample based upon the selectedsecond subset of library candidates, determining a resulting most likelycomposition of the sample based upon the first and second most likelycompositions of the sample, and displaying the resulting most likelycomposition of the sample to a user.

In some embodiments, the method can further include adding a firstwatchlist of library candidates to the first subset of librarycandidates, and, optionally, adding a second watchlist of librarycandidates to the second subset of library candidates. In certainembodiments, the method can further include adding the first most likelycomposition of the sample to the second subset of library candidates. Insome embodiments, the data from the sample can further comprise at leastone observed property of the sample. In certain embodiments, the methodcan further include selecting the second subset of library candidatesprior to selecting the first subset of library candidates and adding thesecond most likely composition of the sample to the first subset oflibrary candidates.

In some embodiments, the similarity of the sample to any single librarycandidate is less than a report threshold value, and the method canfurther include selecting a third subset of library candidates bydetermining a third representation of the similarity of the sample to amixture of library candidates in the first subset of library candidatesusing (i) the first representation of the measured spectrum, (ii) theprecision state of the first representation of the measured spectrum,(iii) the representation of the library spectrum for that librarycandidate, and, optionally, (iv) the precision state of therepresentation of the library spectrum for that library candidate, andwherein determining the resulting most likely composition of the sampleis based on the determined representations of similarity of the sampleto the mixture of library candidates. The report threshold value can begreater than or equal to 0.05. The method can further include selectinga fourth subset of library candidates by determining a fourthrepresentation of the similarity of the sample to a mixture of librarycandidates in the second set of library candidates using (i) the secondrepresentation of the measured spectrum, (ii) the precision state of thesecond representation of the measured spectrum, (iii) the representationof the library spectrum for that library candidate, and, optionally,(iv) the precision state of the representation of the library spectrumfor that library candidate.

In certain embodiments, the method can include adding the first mostlikely composition of the sample to the fourth subset of librarycandidates. Alternatively, the method can include selecting the fourthsubset of library candidates prior to selecting the third subset oflibrary candidates and adding the second most likely composition of thesample to the third subset of library candidates, or, optionally, addingthe second most likely composition of the sample to the first subset oflibrary candidates.

Embodiments also include software in the form of a computer program(“software”) which, when executed by a programmable processor canexecute any one or more of the methods described herein or any one ormore methods which can be executed by any apparatus described here. Suchcomputer program may often be carried in a non-transitory form in asuitable medium, such as a magnetic or optical storage device,solid-state memory, or any other storage medium.

In some embodiments, a computer program product carries a non-transitorycomputer program which, when executed by a process can perform a methodof field analyzing chemical properties of a material, the methodcomprising providing a hand-held instrument comprising at least onesensor adapted for providing Fourier transform infrared spectroscopy(FTIR) surveillance and at least another sensor for providing Ramanspectroscopy surveillance.

In other embodiments, a computer program product carries anon-transitory computer program which, when executed by a process canperform a method of determining the most likely composition of a sampleby at least two technologies using a spectrometer, the method comprisingthe steps described above.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the invention are apparent from thefollowing description taken in conjunction with the accompanyingdrawings in which:

FIG. 1 is an diagram of an instrument suited for practicing theteachings herein;

FIG. 2 is an illustration of components for setting up and managing theinstrument of FIG. 1.

FIG. 3 is an illustration of the stages for use of the instrument ofFIG. 1;

FIG. 4 is a table presenting exemplary considerations for setup and useof the instrument of FIG. 1; and,

FIG. 5 is a table depicting aspects of response profiles for configuringthe instrument of FIG. 1.

FIG. 6 is a flow chart showing a high level overview of the logic usedon a spectrometer (two separate technologies without data fusion).

FIGS. 7A-7B are illustrations of exemplary result screens highlightingthe display of tagged items, here showing 2-propanol in a pure componentmatch (FIG. 7A), and a mixture match (FIG. 7B).

FIG. 8 is a flow chart showing a high level overview of the logic usedon a spectrometer with two separate technologies and including datafusion.

FIG. 9 is a flow chart showing a high level overview of the logic usedon a spectrometer with two separate technologies, including otherexternal data such as at least one observed property of the sample, andincluding data fusion.

FIG. 10 is an illustration of functional groups in the Raman and FTIRwavelength ranges.

FIG. 11 is an illustration of an exemplary result screen highlighting apattern of chemicals that could be used to manufacture other chemicalsof interest.

DETAILED DESCRIPTION

In the description of embodiments presented herein, it is understoodthat a word appearing in the singular encompasses its pluralcounterpart, and a word appearing in the plural encompasses its singularcounterpart, unless implicitly or explicitly understood or statedotherwise. Furthermore, it is understood that for any given component orembodiment described herein, any of the possible candidates oralternatives listed for that component may generally be usedindividually or in combination with one another, unless implicitly orexplicitly understood or stated otherwise. Moreover, it is to beappreciated that the figures, as shown herein, are not necessarily drawnto scale, wherein some of the elements may be drawn merely for clarityof the invention. Also, reference numerals may be repeated among thevarious figures to show corresponding or analogous elements.Additionally, it will be understood that any list of such candidates oralternatives is merely illustrative, not limiting, unless implicitly orexplicitly understood or stated otherwise. In addition, unless otherwiseindicated, numbers expressing quantities of ingredients, constituents,reaction conditions and so forth used in the specification and claimsare to be understood as being modified by the term “about.”

Accordingly, unless indicated to the contrary, the numerical parametersset forth in the specification and attached claims are approximationsthat may vary depending upon the desired properties sought to beobtained by the subject matter presented herein. At the very least, andnot as an attempt to limit the application of the doctrine ofequivalents to the scope of the claims, each numerical parameter shouldat least be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof the subject matter presented herein are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspossible. Any numerical values, however, inherently contain certainerrors necessarily resulting from the standard deviation found in theirrespective testing measurements.

Disclosed herein are methods and apparatus that provide for rapidadaptation of field use instrumentation. Specifically, the solutionsprovided herein enable users to rapidly adjust a combination Fouriertransform infrared (FTIR) and/or Raman spectrometer that is configuredfor field use. Advantageously, the solutions provide for context-basedconfiguring that corresponds to a given type of threat (analysisprofile). Before discussing the solutions and depth, aspects of theinstrumentation are introduced.

Referring now to FIG. 1, there is shown an exemplary instrument 10. Inthis non-limiting example, the instrument 10 provides a user withextensive capabilities for field-based sample analysis. Generally,sample analysis is performed by spectroscopy techniques or technologies.These spectroscopy techniques or technologies can include Fouriertransform infrared (FTIR) spectroscopy and/or Raman spectroscopy. Thatis, the instrument 10 provides for collection of an infrared spectrum ofabsorption, emission, or Raman scattering from a solid, liquid or gassample. The instrument 10 may also be referred to herein as aspectrometer.

The FTIR portion of the instrument 10 illuminates a sample with manyfrequencies of light at once, and measures how much of that beam isabsorbed by the sample. Next, the beam is modified to contain adifferent combination of frequencies, giving a second data point. Thisprocess is repeated many times. Afterwards, a processor on board theinstrument 10 takes the collected data to estimate absorption at eachwavelength. Correlations between absorption data and characteristics forknown materials are then made an output to the user.

The Raman scattering portion of the instrument 10 also illuminates thesample with a beam of light. When photons are scattered from an atom ormolecule in the sample, most photons are elastically scattered (Rayleighscattering), such that the scattered photons have the same energy(frequency and wavelength) as the incident photons. However, a smallfraction of the scattered photons are scattered by an excitation. TheseRaman scattered photons have a frequency different from, and usuallylower than, that of the incident photons. In a sample, Raman scatteringcan occur with a change in energy of a molecule due to a transition. Theinstrument 10 provides resources for collecting an optical signalassociated with the Raman scattering, comparing the optical signal withdata tables, and outputting correlations to the user.

In the exemplary embodiment depicted in FIG. 1, the instrument 10 isprovided as a handheld device. The instrument 10 is contained within ahousing 9. In this embodiment, the housing 9 is “ruggedized.” That is,the housing 9 is configured with features to provide for survival in aharsh environment. Exemplary features for survival include a jacket ofmaterial to protect the exterior of the instrument 10. The jacket ofmaterial may additionally be interchangeable (for example to maintainhygiene of the instrument 10). Additionally, components within thehousing 9 may be shock mounted, surface mounted or otherwise configuredto withstand impact. The housing 9 may further be configured to bemoisture resistant, waterproof and/or to withstand chemical degradation(such as to withstand acidity or alkalinity). For example, theinstrument may be certified to MIL-STD 810G for ruggedness.

The instrument 10 includes a variety of components for enablingsampling, processing, and appropriate outputting of data and/or results.For example, the user is provided with various user controls 11.Generally, the user controls 11 enable user control of the instrument 10for initiation of sampling, processing, and communications.Additionally, the user controls 11 enable the user to configure theinstrument 10, monitor health of the instrument 10 and to perform othersimilar tasks. In some embodiments, the user controls 11 may beconfigured for a particular sampling routine or the like. Generally, thehousing 9 and the user controls 11 are sealed from the environment suchthat the instrument will not be contaminated with sample materials orsubjected to the hazards associated with a given sample.

At least one screen 12 may be provided with the instrument 10.Generally, the screen 12 provides the user with dynamic output. Theoutput may include configuration information, status of the instrument10, semantic information (such as date, time, location information, etc,. . . ), as well as sample analysis information and any otherinformation deemed appropriate. In some embodiments, the screen 12 isprovided as a touch sensitive screen to enable user input through thescreen 12. In an exemplary embodiment, the screen 12 is provided as aliquid crystal display (LCD) with a capacitive overlay to enable touchcapabilities.

In the exemplary embodiment, instrument 10 includes a sampling probe 20as well as an anvil 22. Generally, the sampling probe 20 includes aflexible shaft 23 and at least one sensor 21. However, the probe 20 andsensor 21 may be integrated into the housing 9 in an embodiment withoutshaft 23, and thus provide for “point and shoot” style of sampleanalysis. At least one source of narrowband illumination (not shown) andat least one source of wideband illumination (not shown) may beintegrated into the instrument 10, and may be used with the probe 20 andthe anvil 22. In some embodiments, the anvil 22 is motorized. In someembodiments, the shaft 23 is disposed so that the probe 20 can bepositioned to interrogate the sample while it is in contact with thecollection optics associated with the anvil 22. In this way, a samplecan be interrogated by both the Raman spectrometer and the FourierTransform Infrared spectrometer simultaneously and/or while in a singlelocation.

The at least one source of narrowband illumination may include, forexample, at least one light emitting diode (LED) and/or laser. The atleast one source of wideband illumination may include, for example, atleast one electrically resistive filament and/or membrane. The source ofillumination may further include optical filtering and other componentsas desired for producing optical effects. The instrument 10 may befurther configured to work with external (independent) sources ofillumination. Further, the instrument 10 may be configured to controlthe illumination with respect to sampling. For example, the instrument10 may be configured to change between sources of illumination toprovide adjustments to wavelengths used to illuminate a given sample.Generally, control of the source of illumination in conjunction withsampling is provided through system software.

Generally, the instrumentation includes at least one port 14. The port14 may include a network interface such as an Ethernet, serial,parallel, 802.11, USB, Bluetooth or other type of interface (not shown).The port 14 may be used to provide for remote control, communication ofdata, receipt of output, shared processing, system backup, and othersimilar tasks. In some embodiments, the port 14 provides an interface toan external computer (such as a personal computer (PC)). When theinstrument 10 is connected to a PC (not shown), software installed onthe PC may be used for control and enable rapid configuration of theinstrument 10. As a matter of convention, software installed on anexternal unit (such as a PC configured to provide users with improvedaccess and/or control of the instrument) may generally be referred to asa “profile manager.”

Generally, the instrument 10 includes an internal power supply (e.g., abattery), memory, a processor, a clock, data storage, and other similarcomponents (not shown). Other output devices may further include aspeaker (not shown), such as one configured to provide auditory outputsuch as an alarm. Additional input devices may include a microphone (notshown), such as one configured to receive voice commands from the user.

Generally, the processor is configured to receive input from usercontrols 11 and to control the radiation sources, detection systems andanalysis components. Accordingly, the processor will also provideappropriate information to the output. The instrument 10 may beconfigured to take advantage of robust processing capabilities, and maytherefore include data libraries, substantial memory for data storage,calibration libraries and the like. User controls 11 may include atrigger or other such device to provide for initiation of sampling andanalysis with the spectrometer 10. The output may provide raw data,spectral data, concentration data and other appropriate forms of data.

Generally, the processor is configured to execute application-specificsoftware. That is, the processor is configured to retrieve machineexecutable instructions stored in machine readable media (such as in thememory or the data storage) and provided for enabling the instrument 10to perform a selected method for operation. It should be considered thatany software provided with the instrument 10 may additionally includedata tables, subroutines, links to external resources, and othercomponents as necessary or as deemed appropriate for enabling operation.As one example, the instrument 10 may include at least one library. Theat least one library may include substantial chemical data. Morespecifically, for any given chemical, compound, element or other type ofmaterial, the library may include information such as spectralproperties, identity, dangerous good classification (NFPA labeling)information, material safety data sheet (MSDS) information and the like.As another example, the instrument 10 may include language libraries forconfiguring a user interface according to a language of the user.

As may be surmised, the instrument 10 provides a versatile system. Partof the versatility is realized by the complexity of the instrument 10.By virtue of the complexity of the instrument 10, it is possible toconfigure the instrument 10 for improved performance. That is, aspectssuch as analysis time, order of analyses, power levels and the like maybe configured into specific response profiles according to types ofanalyses, such as analysis of industrial chemicals, homemade explosives,a clandestine drug lab, street narcotics, or chemical warfare agents.More specifically, appropriately adjusting a number of system parametersfor the instrument 10 will improve precision and accuracy for giventypes of analysis.

Aspects of an exemplary system for configuring the spectrometer 10 areprovided in FIG. 2. Referring to FIG. 2, there are shown aspects of asystem for configuring the spectrometer 10. In this exemplaryembodiment, a system manager 26 is configured to communicate with andcontrol the instrument 10. The system manager 26 communicates with theinstrument 10 through network 27. Network 27 may take advantage of anytype of communications protocol deemed appropriate, such as the types ofcommunication discussed above.

Generally, the system manager 26 is provided as machine executableinstructions stored on machine readable media (that is, as “software”that may be executed on a computer, such as a personal computer (PC)).In some embodiments, the system manager 26 provides users withsubstantial information from the instrument 10. For example, the systemmanager 26 can be configured to display at least some of to all of theinternal parameters of the instrument 10. The system manager 26 canfurther allow a user to edit at least some of the internal parameters ofthe instrument 10. Editing of parameters can be performed in a varietyof different ways. For example, the instrument 10 can make use of aconfiguration data file, read only memory (ROM), and other similartechniques as may be known in the art. In some embodiments, the systemmanager 26 can be provided as machine executable instructions stored onmachine readable media, that is, as “software” that can be executed onthe instrument 10.

The system manager 26 (as well as an on-screen user interface of theinstrument 10) can be provided in a variety of different manners. Forexample, user interface schemes can include a graphical user interface(GUI), a text-based interface, and can include files configured fortransfer to another application.

In some embodiments, certain parameters are set in one way for a givenuser or situation, and another way for a different user or situation.Accordingly, the instrument 10 can be configured with various profilesand/or accounts. A system administrator making use of the system manager26 is provided with resources to effectively and conveniently manage thediverse users, accounts and system settings.

For example, it is recognized that there are various stages for use ofor interaction with the instrument 10. More specifically, in a firststage, global settings of the instrument 10 are configured. In a secondphase, a user will configure the instrument 10 for a given surveillance.In the third phase, the user will commence surveillance by analyzingsamples. In a last phase, the user (and/or another party) will reviewand/or analyze data from sample surveillance. Reference may be had toFIG. 3.

FIG. 3 provides an illustration of the stages of use of the instrument10. Additionally, FIG. 4 is a table depicting considerations for a userto evaluate when configuring the instrument 10 for surveillance.

In order to efficiently manage setup of the instrument 10, the systemmanager 26 includes software that provides for context-basedconfigurations. That is, embodiments of the instrument 10 provide userswith an interface that provides for selecting a configuration that ismost appropriate for a given situation.

As discussed herein, a context-based configuration may be referred to asa “response profile.” By using response profiles, a diversity of usersmay use the instrument 10 with relatively little time spent oninstrument set-up or maintenance. This is particularly advantageouswhere diverse users may pass the instrument 10 from one to anotherduring an emergency situation.

Generally, the instrument 10 stores a plurality of response profiles indata storage and/or memory. Each response profile may be configured wellin advance of use, and under controlled conditions. For example, duringinstrument set-up and/or calibration an instruments technician (orengineer, or any other similarly situated party) may determineappropriate settings for a variety of parameters for any given type ofanalysis. As discussed above, the system manager 26 may be used toestablish and/or maintain response profiles stored in the instrument 10.

Accordingly, users are provided with an instrument that isreconfigurable “on the fly.” This means that the bomb disposaldepartment may use the instrument 10 one day and the hazardous materialsresponse team may use the instrument 10 the next day. In some cases,these are the same team. In any case, the product will allow them toselect the configuration associated with their type of call and theinstrument will be ready to go.

Editing of each response profile may be done on the instrument as wellas through the profile manager. Users can also export profiles to anexternal data storage medium and import it onto another instrument.Users are able to add, delete, and edit the settings on profiles as wellas change the profile button icon. An administrator has the ability torestrict some of the profile settings to correspond to department safetyor procedural policies.

Refer now to FIG. 5, where aspects of a plurality of response profiles31 are depicted. As shown in this example, each response profile 31 maybe given an appropriate name such that users may make expedient choicesfor configuring the instrument 10. In this example, the responseprofiles include profiles for: industrial chemicals, homemadeexplosives, a drug laboratory, street narcotics, and chemical warfare.Additionally, a user may want to add a custom response profile 31 andmay be presented with this option. In some embodiments, the user maycopy an existing response profile 31, slightly modify the respectiveresponse profile 31, and then save the modified response profile 31 as anew response profile 31. For example, a user may decide to modifyduration of analyses based on concentrations of compounds encountered inthe field.

Each response profile 31 includes a plurality of parameters 32. Eachparameter 32 is assigned a particular setting 33, or “value.” Parameters32 may include, by way of example and without limitation, a tag list(such as a particular name to be assigned to a given sample), a sessionname (such that upon subsequent review of data, a reviewer may identifysettings for each parameter 32), a technology order (a priority ofprocessing, such that order of processing or sample analysis iscontrolled), instrument pressure (such as for the anvil 22), Raman laserpower (such as for sampling temperature sensitive or explosivematerials), and a plurality of temporal parameters (such as for scanduration, scan delay, scan timeout and the like).

As one may surmise, a great number of parameters 32 may be adjusted toprovide for each response profile 31. The response profiles 31 andparameters 32 shown in FIG. 5 are merely illustrative and are notlimiting of the teachings herein.

Advantageously, the teachings herein provide for combined functionalityof handheld FTIR spectroscopy with handheld Raman spectroscopy. Thisincludes reduction in size and weight (and cost) of the single unit overthe combination of two units. Further advantages include the opportunityto tailor software workflows. Additionally, sample surveillance may besubstantially expedited. For example, the Raman probe may be designed tobe positioned to interrogate the sample while it is in the same locationas for FTIR sampling. That facilitates remote operation of theinstrument using both methodologies, which is particularly important infield applications when samples may be explosive or are otherwise veryhazardous.

These tools have seen increasing adoption for field-based assessment bydiverse users including military, emergency response, and lawenforcement.

Frequently, end-users of portable devices are non-scientists who rely onembedded software and the associated algorithms to convert collecteddata into unambiguous actionable information.

One class of problems commonly encountered in field applications isidentification. Identification algorithms are designed to mine a libraryof known materials and determine whether the unknown measurement isconsistent with a stored response, or combination of stored responses.Such algorithms can be used to identify a material from many thousandsof possible candidates.

A second class of problems is screening. Screening algorithms evaluatewhether at least a subset of features in an unknown measurementcorrespond to one or more specific substances of interest and aretypically configured to evaluate candidates from a small list ofpotential target analytes. As such, screening algorithms are lessbroadly applicable than identification algorithms; however, theytypically provide faster detection rates which makes them attractive forspecific applications such as chemical warfare agent or narcoticsdetection.

Recently, a new approach called tagging has been developed, which is amerging of a screening capability within an identification algorithmframework. Tagging maintains a broad identification capability thatfield users demand (an ability to identify thousands of possible purematerials and trillions of potential mixture candidates), whilesimultaneously providing an extended capability that allows users toconfigure their own tag list (e.g. a user defined set of test targets,also called a watchlist) for enhanced detection of target substances.Given that the list can be rapidly configured in the field, taggingprovides users the ability to incorporate their situational awarenessinto the sample assessment provided by the device. As discussed herein,higher detection rates and lower limits of detection can be achievedwhen items are tagged. Additionally, when identified, tagged items canbe preferentially displayed in the device graphical user interface (GUI)to provide an unambiguous indication to the user that a substance ofinterest has been detected.

Some embodiments described herein are generally directed to an overviewand performance characterization of a combined identification/screeningalgorithm. The combined algorithm provides enhanced detection capabilitymore typical of screening algorithms while maintaining a broadidentification capability. Additionally, this approach can enable usersto incorporate situational awareness during a response.

Algorithm Overview Computational Considerations

As discussed above, contemporary handheld analyzers are increasinglycapable of automatically identifying both pure materials and mixtures.Analysis of unknown mixtures presents special computational challengesthat cannot be ignored. Modern reference databases frequently contain inexcess of 10,000 library spectra, and some mixture algorithms deployedon handheld devices attempt to simultaneously fit up to 5 mixturecomponents. The number of potential mixture solution candidates that canbe evaluated for a given library can be calculated using the followingformula:

$\begin{matrix}{N = \frac{n!}{{k!}{\left( {n - k} \right)!}}} & (1)\end{matrix}$

where N is the number of possible mixture candidates, n is the number oflibrary reference spectra, and k is the maximum number of mixturecomponents that are simultaneously fit. Based on the formula shownabove, the number of possible mixture combinations scales rapidly withthe number of components that are simultaneously fit, especially forlarge reference library databases. To illustrate this point, Table 1captures the number of possible mixture candidates for a referencelibrary containing 10,000 items when 2-5 component solutions areconsidered.

TABLE 1 Number of potential mixture candidates (N) as a function of thenumber of mixture components fit (k) for a 10,000 item library n k N10,000 2 4.9995 × 10⁷  10,000 3 1.6662 × 10¹¹ 10,000 4 4.1642 × 10¹⁴10,000 5    >1 × 10¹⁵

It can be seen from Table 1 that with a 10,000 item library, there arealmost 50 million possible two component mixture candidates alone.Further complicating the issue is that handheld devices typically havelimited on-board computing power. With the on-board processingcapability available on portable devices today, it would take days toevaluate every potential mixture solution that could be generated from alarge, modern reference database. As a result of the computationalexpense associated with the mixture problem, it is common for handheldidentification devices to incorporate a rapid calculation that can beused to down select the library to a reduced and therefore moremanageable number of entries. The down selection is a preliminary stepperformed prior to final analysis by more precise pure component andmixture analysis algorithms.

Shown in FIG. 6 is a flow chart illustrating an exemplary high leveloverview of an identification algorithm used in a handheldidentification device without data fusion, that is, without integrationof spectral information from different technologies. Further descriptionof such an identification algorithm can also be found in U.S. Pat. No.7,254,501, entitled: “SPECTRUM SEARCHING METHOD THAT USES NON-CHEMICALQUALITIES OF THE MEASUREMENT,” issued to Brown et al., and assigned tothe assignees of the present application, the disclosure of which ishereby incorporated by reference in its entirety. However, whereanything in the incorporated reference contradicts anything stated inthe present application, the present application prevails. As noted inthe flow chart, down selection is performed prior to pure componentassessment and mixture analysis. The intention of this approach is toenable rapid analysis times for end users and ensure that the majorityof computational time is allotted to making a detailed assessment of theunknown sample against select library items of interest.

While optimized for computational efficiency, the down selectionprocedures are also designed to retain reference library candidates mostlikely to be present in the unknown spectrum. The down selectionstrategy is not lossless, however, so it is possible for items that arepresent in the unknown sample to be omitted from consideration prior toanalysis by the final algorithms. For pure materials this is rarely anissue; however, this does become more of a difficulty for minor mixturecomponents whose features in the unknown spectrum may be largely maskedby the dominant mixture component(s). One aspect of the enhancedscreening capability provided by tagging is to reduce the likelihoodthat a tagged substance (i.e., a substance included on a watchlist) willbe erroneously dismissed from consideration prior to the final analysisalgorithms. Further description of tagging can also be found in U.S.patent application Ser. No. 13/540,152, entitled: “METHOD FOR TAGGINGREFERENCE MATERIALS OF INTEREST IN SPECTROSCOPIC SEARCHINGAPPLICATIONS,” to Green et al., and assigned to the assignees of thepresent application, the disclosure of which is hereby incorporated byreference in its entirety. However, where anything in the incorporatedreference contradicts anything stated in the present application, thepresent application prevails.

In the flow chart shown in FIG. 6, two methods 601 and 602 ofdetermining the most likely composition of a sample by at least twotechnologies (e.g., FTIR as Technology 1 and Raman as Technology 2)using a spectrometer include at step 610-1 obtaining data from thesample by a first technology (Technology 1) using the spectrometer,wherein the data comprises a first representation of a measured spectrumobtained by the first technology, and at step 610-2 obtaining data fromthe sample by a second technology (Technology 2) using the spectrometer,wherein the data comprises a second representation of a measuredspectrum obtained by the second technology. The methods then include atsteps 620-1 or 620-2 (for the first or second method 601 or 602,respectively) determining a precision state of the first or secondrepresentation of the measured spectrum, providing a first or second setof library candidates and, for each library candidate, providing datarepresenting each library candidate, wherein the data comprises arepresentation of a library spectrum obtained by the first or secondtechnology, selecting a first or second subset of library candidates bydetermining a first or second representation of the similarity of thesample to each library candidate in the first or second set of librarycandidates using (i) the first or second representation of the measuredspectrum, (ii) the precision state of the first or second representationof the measured spectrum, (iii) the representation of the libraryspectrum for that library candidate, and (iv) the precision state of therepresentation of the library spectrum for that library candidate, anddetermining a first or second most likely composition of the samplebased upon the selected first or second subset of library candidates.The methods 601 and 602 can optionally include, at steps 615-1 or 615-2,adding a first or second watchlist of library candidates to the first orsecond subset of library candidates.

The critical question to be answered by the spectral library searchappliance is: given the instrumental measurement of the specimen, andthe conditions under which it was measured, (1) is it probable that anyof the library records are a match?, and (2) what are the probabilitiesPA, PB . . . that the measured material is in fact A, B, etc.? Theseprobabilities must be directly dependent on the measurement data, andits quality. Generally speaking, the measurement quality is a functionof the accuracy of the measurement and its precision (or variability).It can often be assumed that, if the instrument has been designedappropriately and/or appropriate signal conditioning methods have beenused, the measurement will be reasonably accurate, but inevitablysuffers from imprecision to a degree dependent on the measurementconditions.

The method collects data and measures sources of uncertainty. For adispersive Raman spectrometer measurement using charge coupled device(CCD) detection, as an example, many distinct sources of variabilitycontribute to Σ_(means), the precision state of the measurement:

Σ_(meas) =f(I _(Ral) ,I _(Ram) ,I _(fl) ,I _(ambient) ,I_(dark),σ_(read) ,Q,D _(CCD) ,G _(CCD) ,C,T,H,t,L)  (2)

where I_(Ral) is the Raleigh scatter intensity, I_(Ram) is the Ramanscatter intensity, I_(fl) is the fluorescence intensity, and I_(ambient)is the ambient light intensity.

All of the terms listed in Eq. 2 affect the uncertainty of theanalytical measurement because they each contribute photon shot noise.I_(dark) is the dark current intensity in the CCD, the spontaneousaccumulation of detector counts without impinging photons, which alsocontributes shot noise. σ_(read) is the read noise (imprecision inreading out the CCD response), Q is quantization error (a consequence ofthe analog-to-digital conversion ADC), D_(CCD) is a term relating tovariability that is a consequence of defects in the CCD construction,G_(CCD) is the gain on the CCD (the conversion factor from electrons tocounts), T and H are the temperature and humidity conditions of themeasurement, t is the time spent integrating the signals, C isphysicochemical effects that can alter the exact Raman intensities ofthe sample (note that each of these effects has a potential wavelengthdependence), and L is a “long-term” variability term that reflectschanges in the system performance over a time period greater than thatof any individual sample measurement, e.g., calibration relatedvariability. As is apparent from the above discussion, some sources ofimprecision are determined by the measurement conditions (e.g., photonshot noise, dark noise), some are determined by the unit taking themeasurements (e.g., system gain, read noise, quantization noise), andsome are determined by the overall design of the platform (e.g.,wavelength axis and linewidth stability, temperature/humiditysensitivity).

Once the scan data arrives at a signal-to-noise-ratio (SNR) thresholddeemed sufficient for this chemical identification, the result is theidentification of chemical X. The null hypothesis states that themeasurement spectrum belongs to the population of the reference libraryspectrum given the measurement uncertainty. The alternative hypothesisstates that the measurement spectrum does not belong to the populationof the reference library spectrum.

In statistics, the p-value is the probability of obtaining the observedsample results (or a more extreme result) when the null hypothesis isactually true. If this p-value is very small, usually less than or equalto a report threshold value previously chosen called the significancelevel (traditionally 95%), it suggests that the observed data isinconsistent with the assumption that the null hypothesis is true, andthus that hypothesis must be rejected. Thus, if a p-value (herein alsocalled a report threshold value) is greater than or equal to 0.05, themeasurement is considered consistent with the reference spectrum and thedevice will report a positive match (e.g., by displaying a greenscreen). Otherwise, the device may undertake mixture analysis, or willreport similar items, as described below, or will report no matchdepending on the unit configuration.

If at step 625-1 or 625-2 the sample is a pure sample, determined asdescribed further below, then step 640-1 or 640-2 displays the purematch, or, if the pure match chemical is known by more than one name,then step 640-1 or 640-2 displays the pure matches. If at step 625-1 or625-2 the sample is not a pure sample, that is, if the similarity of thesample to any single library candidate is less than a report thresholdvalue, which, as described above, can be greater than or equal to 0.05,then the methods 601 and 602 further include at steps 650-1 or 650-2selecting a third or fourth subset of library candidates by determininga third or fourth representation of the similarity of the sample to amixture of library candidates in the first or second subset,respectively, of library candidates using (i) the first or secondrepresentation of the measured spectrum; (ii) the precision state of thefirst or second representation of the measured spectrum; (iii) therepresentation of the library spectrum for that library candidate; and,optionally, (iv) the precision state of the representation of thelibrary spectrum for that library candidate. Determining the resultingmost likely composition of the sample is based on the determinedrepresentations of similarity of the sample to the mixture of librarycandidates, that is, based on a determination that the similarity of thesample to a mixture of library candidates is greater than a reportthreshold value, which, as described above, can be greater than or equalto 0.05. The methods 601 and 602 can optionally include, at steps 645-1or 645-2, adding a third or fourth watchlist of library candidates tothe third or fourth subset of library candidates. At step 655-1 or655-2, if the mixture is recognized (p-value greater than or equal to0.05), then step 670-1 or 670-2 displays the mixture result. If themixture is not recognized, then at step 675-1 or 675-2, if there are anysimilar items, that is, any items resulting in a p-value greater than1×10⁻⁴ and less than 0.05, then step 680-1 or 680-2 displays the similaritems. If there are no similar items, then step 690-1 or 690-2 reportsno match found.

Reporting Considerations

A final topic not explicitly shown in FIG. 6 is the reporting criteriaassociated with automated mixture algorithms. For the analysis to betruly automated, goodness of fit thresholds must be set in the algorithmto determine which component(s), if any, will be reported. The need toset reporting thresholds is universal and does not depend on the type ofanalysis algorithm that is used.

Selection of the reporting threshold value has a direct impact on thetradeoff between true positive rate (TPR) and false positive rate (FPR)of the search appliance. In a typical unknown identification scenario,such as a hazardous material identification (e.g., a hazmat call),special consideration is made to ensure that the FPR is kept low. Thisprevents the user from acting on information that may be ambiguous andallows them to focus time and efforts on other assessments that mayprovide more definitive information. By contrast, in a screeningscenario, the reporting thresholds are often set to maximize TPR, evenat the expense of a higher FPR. This is because in screening scenarios,such as medical diagnostic testing, there is often a second confirmatorytest that can be performed in order to mitigate false positives.

Based on these considerations, another reason that screening algorithmsare capable of providing better detection rates than standardidentification algorithms is that the reporting thresholds are optimizedfor substances of interest. Optimization of the reporting thresholds fortagged items is expected to result in a much better detectioncapability, although a slight increase in false alarm rate may beexpected as well.

Field Considerations

End users of identification equipment frequently have informationavailable to them from a variety of sources. During a typical response,information in the form of other external data intelligence may beavailable in the form of product labels, sample observations (solid,liquid, or gas, color, odor), pH measurements, and test results from avariety of analyzers. As users begin to evaluate all of the informationavailable to them, they make assessments about what potential materialsare most likely to be present in the unknown sample. Traditionalidentification equipment and algorithms have no way of incorporatingreal time information, or situational awareness, into the identificationassessment provided by the device. As demonstrated above, the taggingapproach provides higher detection rates and lower limits of detectionthan a typical ‘blind’ identification algorithm. Since the instrument isdesigned so that the tag list (i.e., the watchlist) can be adapted,modified, or edited at any time, the tagging method provides a novelcapability for instrument operators to better incorporate knowledgegained during the course of a response.

Another benefit of tagging is that when tagged items are identified, thetagged items can be displayed to the user with an icon next to them,providing an unambiguous indication to the end user that a substance ofinterest has been identified. To ensure that the tag icon is obvious tothe end user, mixtures can be displayed with tagged substances appearingat the top, regardless of the spectral contribution (e.g., weight) forthat substance. FIGS. 7A and 7B show example result displays for a purecomponent match (FIG. 7A), and a mixture match (FIG. 7B). The respectivematches can be highlighted in a variety of ways, such as using differentcolor screens.

While the focus herein has been most heavily on the enhanced detectioncapabilities that tagging provides, the GUI elements described hereinprovide substantial benefits in their own right. For many applications,the threat landscape is constantly expanding or changing. As a result,it becomes difficult for end users to keep abreast of new substances ofinterest and there may be uncertainty regarding which substances are ofmost concern. With tagging in place, users no longer need to remember along list of threat materials to watch for. Instead, they are trainedthat any result showing a red flag warrants escalation.

Data Fusion

As has been described above, a screening algorithm termed ‘tagging’ hasbeen deployed onto handheld identification devices, such asspectrometers. This concept can also be deployed on a spectrometer thatcombines two or more technologies. In the first instance, a profile canbe setup on the device.

The profile can include several settings:

Raman laser power,

Raman scan delay,

Raman scan timeout,

FTIR anvil force,

FTIR scan delay,

FTIR scan timeout, and

a tag list of items relevant to the profile.

A tag list (also called a watchlist herein) of items can be of anytechnology; in the case described herein, the two technologies are Ramanand FTIR spectroscopy. By selecting a chemical in the library, thatchemical, whether Raman only, FTIR only or covered by both technologies,will be searched when a scan of that technology is performed. Examplesof pure chemicals include explosive materials, such as triacetonetriperoxide (TATP), RDX, and hexamethylene triperoxidediamine (HMTD),toxic materials, such as acrolein, chlorosulfonic acid, isopropylisocyanate, and toluene 2,4-diisocyanate, and narcotic materials, suchas heroin HCl, cocaine freebase, methamphetamine HCl, and JWH-018.Examples of mixtures of chemical include cocaine HCl/benzocaine, heroinHCl/acetaminophen (common narcotic mixtures), 2-propanol/methanol,ethanol/water, methyl ethyl ketone/isopropanol/ethanol, andacetaminophen/a-Lactose monohydrate. FIG. 6 illustrates the data flow oftwo separate technologies, that could be two completely separate devicesor technologies where no a priori knowledge is passed into the decisionengine for either technology, both operating completely separately.

A significant possibility when two technologies are combined into thesame device, is the possibility to share information between them. Twopossibilities described below are 1) to pass information before, and 2)after a measurement.

Before (Pre-Data Collection)

In the first instance, the possibilities encountered when passinginformation obtained from a first technology before a scan has beeninitiated by a second technology are considered. In FIG. 8, the flow ofcollecting data using one technology is described. When Technology 1 hasgiven a result, those identifications are passed into the subset of datafor consideration by Technology 2, that is those items are considered inthe same manner as tagging, but have resulted from an identification byTechnology 1.

In the flow chart shown in FIG. 8, two methods 801 and 802 ofdetermining the most likely composition of a sample by at least twotechnologies (e.g., FTIR as Technology 1 and Raman as Technology 2)using a spectrometer and including data fusion include at step 810-1obtaining data from the sample by a first technology (Technology 1)using the spectrometer, wherein the data comprises a firstrepresentation of a measured spectrum obtained by the first technology,and at step 810-2 obtaining data from the sample by a second technology(Technology 2) using the spectrometer, wherein the data comprises asecond representation of a measured spectrum obtained by the secondtechnology. The methods then include at steps 820-1 or 820-2 (for thefirst or second method 801 or 802, respectively) determining a precisionstate of the first or second representation of the measured spectrum,providing a first or second set of library candidates and, for eachlibrary candidate, providing data representing each library candidate,wherein the data comprises a representation of a library spectrumobtained by the first or second technology, selecting a first or secondsubset of library candidates by determining a first or secondrepresentation of the similarity of the sample to each library candidatein the first or second set of library candidates using (i) the first orsecond representation of the measured spectrum, (ii) the precision stateof the first or second representation of the measured spectrum, (iii)the representation of the library spectrum for that library candidate,and, optionally, (iv) the precision state of the representation of thelibrary spectrum for that library candidate, and determining a first orsecond most likely composition of the sample based upon the selectedfirst or second subset of library candidates. The methods 801 and 802can optionally include, at steps 815-1 or 815-2, adding a first orsecond watchlist of library candidates to the first or second subset oflibrary candidates.

If at step 825-1 or 825-2 the sample is a pure sample, determined asdescribed further below, then step 840-1 or 840-2 displays the purematch, or, if the pure match chemical is known by more than one name,then step 840-1 or 840-2 displays the pure matches. In contrast tomethods 601 and 602, however, the pure match, or first most likelycomposition of the sample displayed at step 840-1 is added to the secondsubset of library candidates obtained at step 820-2. Alternatively, thesecond most likely composition of the sample displayed at step 840-2 isadded to the first subset of library candidates obtained at step 820-1.

If at step 825-1 or 825-2 the sample is not a pure sample, that is, ifthe similarity of the sample to any single library candidate is lessthan a report threshold value, which, as described above, can be greaterthan or equal to 0.05, then the methods 801 and 802 further include atsteps 850-1 or 850-2 selecting a third or fourth subset of librarycandidates by determining a third or fourth representation of thesimilarity of the sample to a mixture of library candidates in the firstor second subset, respectively, of library candidates using (i) thefirst or second representation of the measured spectrum, (ii) theprecision state of the first or second representation of the measuredspectrum, (iii) the representation of the library spectrum for thatlibrary candidate; and, optionally, (iv) the precision state of therepresentation of the library spectrum for that library candidate.Determining the resulting most likely composition of the sample is basedon the determined representations of similarity of the sample to themixture of library candidates, that is, based on a determination thatthe similarity of the sample to a mixture of library candidates isgreater than a report threshold value, which, as described above, can begreater than or equal to 0.05. The methods 801 and 802 can optionallyinclude, at step 845-1 or 845-2, adding a third or fourth watchlist oflibrary candidates to the third or fourth subset of library candidates.In contrast to methods 601 and 602 shown in FIG. 6, however, the purematch, or first most likely composition of the sample displayed at step840-1 is added to the fourth subset of library candidates at step 850-2.

At step 855-1 or 855-2, if the mixture is recognized, (p-value greaterthan or equal to 0.05), then step 870-1 or 870-2 displays the mixtureresult. If the mixture is not recognized, then at step 875-1 or 875-2,if there are any similar items, that is, any items resulting in ap-value greater than 1×10⁻⁴ and less than 0.05, then step 880-1 or 880-2displays the similar items. If there are no similar items, then step890-1 or 890-2 reports no match found. In contrast to methods 601 and602, however, the mixture result displayed at step 870-1 or the similaritems displayed at step 880-1 are added to the second watchlist at step815-2 and the fourth watchlist at step 845-2. Alternatively, the mixtureresult displayed at step 870-2 or the similar items displayed at step880-2 are added to the first subset of library candidates obtained atstep 820-1 or the third subset of library candidates obtained at step850-1.

Further to this concept, information about a sample could be passed intoan algorithm making decisions around which data to pass within thealgorithm. A user could provide information about the physical state ofthe sample, such as its form (solid, liquid, or gas (e.g., in acontainer)), or its color. These attributes could be used to include orrule out items being considered by the algorithm.

In the flow chart shown in FIG. 9, two methods 901 and 902 ofdetermining the most likely composition of a sample by at least twotechnologies (e.g., FTIR as Technology 1 and Raman as Technology 2)using a spectrometer and including data fusion include at step 910-1obtaining data from the sample by a first technology (Technology 1)using the spectrometer, wherein the data comprises a firstrepresentation of a measured spectrum obtained by the first technology,and at step 910-2 obtaining data from the sample by a second technology(Technology 2) using the spectrometer, wherein the data comprises asecond representation of a measured spectrum obtained by the secondtechnology. The methods then include at steps 920-1 or 920-2 (for thefirst or second method 901 or 902, respectively) determining a precisionstate of the first or second representation of the measured spectrum,providing a first or second set of library candidates and, for eachlibrary candidate, providing data representing each library candidate,wherein the data comprises a representation of a library spectrumobtained by the first or second technology, selecting a first or secondsubset of library candidates by determining a first or secondrepresentation of the similarity of the sample to each library candidatein the first or second set of library candidates using (i) the first orsecond representation of the measured spectrum, (ii) the precision stateof the first or second representation of the measured spectrum, (iii)the representation of the library spectrum for that library candidate,and, optionally, (iv) the precision state of the representation of thelibrary spectrum for that library candidate, and determining a first orsecond most likely composition of the sample based upon the selectedfirst or second subset of library candidates. The methods 901 and 902can optionally include, at steps 915-1 or 915-2, adding a first orsecond watchlist of library candidates to the first or second subset oflibrary candidates.

If at step 925-1 or 925-2 the sample is a pure sample, determined asdescribed further below, then step 940-1 or 940-2 displays the purematch, or, if the pure match chemical is known by more than one name,then step 940-1 or 940-2 displays the pure matches.

If at step 925-1 or 925-2 the sample is not a pure sample, that is, ifthe similarity of the sample to any single library candidate is lessthan a report threshold value, which, as described above, can be greaterthan or equal to 0.05, then the methods 901 and 902 further include atsteps 950-1 or 950-2 selecting a third or fourth subset of librarycandidates by determining a third or fourth representation of thesimilarity of the sample to a mixture of library candidates in the firstor second subset, respectively, of library candidates using (i) thefirst or second representation of the measured spectrum, (ii) theprecision state of the first or second representation of the measuredspectrum, (iii) the representation of the library spectrum for thatlibrary candidate, and, optionally, (iv) the precision state of therepresentation of the library spectrum for that library candidate.Determining the resulting most likely composition of the sample is basedon the determined representations of similarity of the sample to themixture of library candidates, that is, based on a determination thatthe similarity of the sample to a mixture of library candidates isgreater than a report threshold value, which, as described above, can begreater than or equal to 0.05. The methods 901 and 902 can optionallyinclude, at step 945-1 or 945-2, adding a third or fourth watchlist oflibrary candidates to the third or fourth subset of library candidates,respectively.

At step 955-1 or 955-2, if the mixture is recognized, (p-value greaterthan or equal to 0.05), then step 970-1 or 970-2 displays the mixtureresult. If the mixture is not recognized, then at step 975-1 or 975-2,if there are any similar items, that is, any items resulting in ap-value greater than 1×10⁻⁴ and less than 0.05, then step 980-1 or 980-2displays the similar items. If there are no similar items, then step990-1 or 990-2 reports no match found.

In contrast to methods 601 and 602, however, the pure match, or firstmost likely composition of the sample displayed at step 940-1, and/orthe second most likely composition of the sample displayed at step940-2, and/or the mixture result displayed at step 970-1, and/or themixture result displayed at step 970-2, and/or the similar itemsdisplayed at step 980-1, and/or the similar items displayed at step980-2 are combined in the data fusion step 992 together with otherexternal data intelligence, step 991. If the data is recognized at step995, then the recognized data is displayed at step 996, otherwise nomatch found is reported at step 997.

Additionally or alternatively, an image of the sample could be captured,which could then be analyzed by an image algorithm, to determine itsphysical state as described above, but completely autonomously, with noinput from the user(s). If the image analysis is able to determine thephysical state of the sample, data analysis could be managed, so that anidentification of sample that does not match the sample state would notbe displayed to the end-user.

After (Post-Data Collection)

Data analysis after data collection can take place in a multitude ofdifferent routes. As described below, possible options includereanalysis of data, using combined results, to offer betteridentification performance, external data/sample information, analysisof the spectrum for key spectral features (functional group analysis),and analysis of previous scan identification results for chemicalpatterns.

Analysis & Reanalysis of Data, Using Combined Results, to Offer BetterIdentification Performance

Data from one or more technologies can be combined to afford one result,whether that is a pure chemical, or mixture of chemicals. Twotechnologies can operate completely separated, as shown in FIG. 6, orthe results can be combined, as shown in FIGS. 8 and 9, either after theresult has been shown to a user or before. Examples of the varioussituations are described in Table 2.

TABLE 2 Situation 1 Situation 2 Situation 3 Situation 3 Situation 4Technology 1 Technology 1 Technology 1 Technology 1 Technology 1 Result:Result: chemical X Result: chemical X Result: chemical X Result:chemical X chemical X Technology 2 Technology 2 Technology 2 Technology2 Technology 2 Result: chemical Y Result: chemical Y Chemical X forcedResult: chemical Y Result: Presented to user: Presented to user: intoconsideration Sample chemical X Two separate Resulted during Tech 2information Presented to result screens for combined analysis stagesprovided, rules user: Chemical X & Y One result screen Presented touser: out chemical Y Two separate for Chemicals X One result screenPresented to user: result screens & Y for Chemicals X & Y One resultscreen for Chemical X for Chemicals X

External Data/Sample Information

Further to the pre-analysis stage, information about a sample could bepassed into an algorithm analyzing one or more scan data spectra. A usercould provide information about the physical state of the sample in theform of other external data intelligence shown in FIG. 9, such as itsform (solid, liquid, or gas (e.g., in a container)), or its color. Theseattributes could be used to include or rule out candidates beingconsidered by the algorithm.

Additionally or alternatively, an image of the sample could be captured,which could then be analyzed by an image algorithm, to determine itsphysical state as described above, but completely autonomously, with noinput from the user(s). If the image analysis is able to determine thestate of the sample, data analysis could be managed, so that anidentification of sample that does not match the sample state would notbe displayed to the end-user.

Analysis of the Spectrum for Key Spectral Features (Functional GroupAnalysis)

Analysis post data collection could also analyze the spectral featuresobtained by Raman, mid-IR (FTIR), and NIR spectroscopy. Analysis of thespectra could impart additional or secondary information. Analysis ofthe spectrum and corroboration of the identification results between twocomplementary technologies could be beneficial.

A spectrum is unique to the chemical being measured. Using thisattribute, through analysis of the spectrum and analysis of thefunctional groups present, examples of which are shown in FIG. 10, ananalysis post data collection may be able to confirm or refute theidentification put forwarded by the spectrometer or offer additionalsample information if the spectrometer was unable to definitivelyidentify the chemical(s).

Analysis of Previous Scan Identification Results for Chemical Patterns.

The handheld identification device can analyze scan data informationthat may have been collected in the same data session (folder) using thesame scan profile. The device could analyze the scan identificationresults within a session to look for patterns of chemicals that could beused to manufacture illicit chemicals (e.g., in an investigation of aclandestine laboratory). For instance, the device could have identifiedhydrogen peroxide, sulfuric acid and acetone, using either the sametechnology or two different technologies. As shown in FIG. 11, thedevice could recognize that those three chemicals are used tomanufacture Triacetone triperoxide (TATP), which is a homemade explosive(HME) chemical. Such a device and capability could also be used in, butnot limited to, the following environments: a chemical warfare lab, anexplosive clandestine lab, or a narcotic clandestine lab. Such a devicecould be directed by an end-user to analyze possibilities with aselected group of chemicals or work autonomously with no end-user input.

Having thus introduced aspects of the invention, some further featuresand embodiments are now presented.

In one embodiment, the instrument 10 has a weight of about four pounds.It has a size of about eight inches by about four inches by about twoand one half inches. The probe 20 may be used in a hand-held mode, avial mode or mounted to another apparatus such as a robot. The probe 20can be operated over a spectral range of 100 cm⁻¹ to 3000 cm⁻¹, such asa spectral range of 250 cm⁻¹ to 2850 cm⁻¹, at a spectral resolution in arange from about 5 cm⁻¹ to 11 cm⁻¹, such as a range from about 7 cm⁻¹ to10.5 cm⁻¹. The power adjustable laser output can be in a range betweenabout 50 mW to about 300 mW, such as a range between about 75 mW toabout 250 mW. The anvil 22 can be operated over a spectral range ofbetween about 650 cm⁻¹ to about 4,000 cm⁻¹, at a spectral resolution ofabout 4 cm⁻¹. Collection optics for the anvil 22 may include a soliddiamond crystal ATR.

The instrument 10 may exhibit survivability that meets the requirementsof MIL-STD-810G and IP67 standards. Sampling exposure may be in a manualor automatic mode. Scan delay may be user configurable with the delay upto, for example, about 120 seconds. The power supply may includeremovable and rechargeable batteries, such as lithium-ion batteries. Anexternal power supply may be connected to the instrument 10, and provideabout 12 V at about 1.25 A. The instrument may be operated at atemperature range of about minus 4 degrees Fahrenheit to about 122degrees Fahrenheit on a continuous basis.

A variety of known programming and interface techniques may be used toprovide for generation and/or adaptation of the response profiles 31.For example, a response profile builder may be provided. The responseprofile builder may be used to query a user on desired settings for aplurality of parameters 32. In some embodiments, the response profilebuilder is maintained on board the instrument 10, and may be furtherconfigured by the response profile manager on board a PC. In thismanner, a user may quickly select and copy an existing response profile31, and then step through a series of menus to adjust commonly usedparameters 32.

It will be appreciated that any embodiment of the present invention mayhave features additional to those cited. Sometimes the term “at least”is used for emphasis in reference to a feature. However, it will beunderstood that even when “at least” is not used, additional numbers ortypes of the referenced feature may still be present. The order of anysequence of events in any method recited in the present application isnot limited to the order recited. Instead, the events may occur in anyorder, including simultaneously, which is logically possible.

Various other components may be included and called upon for providingfor aspects of the teachings herein. For example, additional electroniccomponents as well as software, combinations of electronic components aswell as software and/or omission thereof may be used to provide foradded embodiments that are within the scope of the teachings herein.

As discussed herein, the term “software” generally refers to aninstruction set provided as machine executable instructions provided asa non-transitory signal, such as stored on machine readable media.Generally, the software provides for enhanced functionality of theinstrument 10. It is not a requirement however that such software residein memory of the instrument 10. For example, software that is used on anexternal computer, such as a PC, to provide for configuration of theinstrument 10 by use of a robust computing platform is contemplated bythe teachings herein. As discussed herein, the “software” may bedownloaded to the instrument, stored in the instrument, or otherwisereside in the instrument. For example, the software may be provided inread only memory (ROM) in a manner commonly referred to as “firmware.”

Exemplary tools for providing at least some of the software disclosedherein include LINUXQT, from DIGIA of Finland. LinuxQT is across-platform application framework that is widely used for developingapplication software with a graphical user interface (GUI). Othercomparable or desired tools may be used.

As discussed herein, the instrument is generally provided as a“handheld” instrument. This is not to imply that the instrument must fitwithin one's hand. That is, the instrument may have any form factor thatis appropriate for field use. Accordingly, use of shared processing andother techniques to limit the size or otherwise configured theinstrument are contemplated by the teachings herein. Generally, theinstrument presented herein need merely be defined as adequate forsupporting the sampling and analysis needs of field personnel as deemedappropriate by a user, designer, manufacturer or other similarlyinterested party.

When introducing elements of the present invention or the embodiment(s)thereof, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. Similarly, the adjective“another,” when used to introduce an element, is intended to mean one ormore elements. The terms “including” and “having” are intended to beinclusive such that there may be additional elements other than thelisted elements.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications will be appreciated by those skilled in theart to adapt a particular instrument, situation or material to theteachings of the invention without departing from the essential scopethereof. Therefore, it is intended that the invention not be limited tothe particular embodiment disclosed as the best mode contemplated forcarrying out this invention, but that the invention will include allembodiments falling within the scope of the appended claims.

What is claimed is:
 1. A spectrometer configurable for field analyses ofchemical properties of a material, the spectrometer comprising: ahand-held instrument comprising at least one sensor adapted forproviding Fourier transform infrared spectroscopy (FTIR) surveillanceand at least another sensor for providing Raman spectroscopysurveillance.
 2. The spectrometer of claim 1, further including a useraccessible instruction set for modifying a sampling configuration of thespectrometer.
 3. The spectrometer of claim 1, further including aplurality of user accessible response profiles, each response profileproviding an instruction set for modifying a sampling configuration ofthe spectrometer.
 4. A method of determining the most likely compositionof a sample by at least two technologies using a spectrometer, themethod comprising: a. obtaining data from the sample by a firsttechnology using the spectrometer, wherein the data comprises a firstrepresentation of a measured spectrum obtained by the first technology;b. determining a precision state of the first representation of themeasured spectrum; c. providing a first set of library candidates and,for each library candidate, providing data representing each librarycandidate, wherein the data comprises a representation of a libraryspectrum obtained by the first technology; d. selecting a first subsetof library candidates by determining a first representation of thesimilarity of the sample to each library candidate in the first set oflibrary candidates using (i) the first representation of the measuredspectrum; (ii) the precision state of the first representation of themeasured spectrum; and (iii) the representation of the library spectrumfor that library candidate; e. determining a first most likelycomposition of the sample based upon the selected first subset oflibrary candidates; f. obtaining data from the sample by a secondtechnology using the spectrometer, wherein the data comprises a secondrepresentation of a measured spectrum obtained by the second technology;g. determining a precision state of the second representation of themeasured spectrum; h. providing a second set of library candidates and,for each library candidate, providing data representing each librarycandidate, wherein the data comprises a representation of a libraryspectrum obtained by the second technology; i. selecting a second subsetof library candidates by determining a second representation of thesimilarity of the sample to each library candidate in the second set oflibrary candidates using (i) the second representation of the measuredspectrum; (ii) the precision state of the second representation of themeasured spectrum; and (iii) the representation of the library spectrumfor that library candidate; j. determining a second most likelycomposition of the sample based upon the selected second subset oflibrary candidates; k. determining a resulting most likely compositionof the sample based upon the first and second most likely composition ofthe sample; and l. displaying the resulting most likely composition ofthe sample to a user.
 5. The method of claim 4, wherein the selecting of(d) additionally uses the precision state of the representation of thelibrary spectrum for that library candidate.
 6. The method of claim 4,wherein the selecting of (i) additionally uses the precision state ofthe representation of the library spectrum for that library candidate.7. The method of claim 4, further including adding a first watchlist oflibrary candidates to the first subset of library candidates.
 8. Themethod of claim 7, further including adding a second watchlist oflibrary candidates to the second subset of library candidates.
 9. Themethod of claim 4, further including adding the first most likelycomposition of the sample to the second subset of library candidates.10. The method of claim 9, wherein the data from the sample furthercomprises at least one observed property of the sample.
 11. The methodof claim 4, further including selecting the second subset of librarycandidates prior to selecting the first subset of library candidates andadding the second most likely composition of the sample to the firstsubset of library candidates.
 12. The method of claim 4, wherein thesimilarity of the sample to any single library candidate is less than areport threshold value, and the method further includes selecting athird subset of library candidates by determining a third representationof the similarity of the sample to a mixture of library candidates inthe first subset of library candidates using (i) the firstrepresentation of the measured spectrum; (ii) the precision state of thefirst representation of the measured spectrum; and (iii) therepresentation of the library spectrum for that library candidate, andwherein determining the resulting most likely composition of the sampleis based on the determined representations of similarity of the sampleto the mixture of library candidates.
 13. The method of claim 12,wherein the selecting of the third subset of library candidatesadditionally uses the precision state of the representation of thelibrary spectrum for that library candidate.
 14. The method of claim 12,wherein the report threshold value is greater than or equal to 0.05. 15.The method of claim 12, further including selecting a fourth subset oflibrary candidates by determining a fourth representation of thesimilarity of the sample to a mixture of library candidates in thesecond set of library candidates using (i) the second representation ofthe measured spectrum; (ii) the precision state of the secondrepresentation of the measured spectrum; and (iii) the representation ofthe library spectrum for that library candidate.
 16. The method of claim15, wherein the selecting of the fourth subset of library candidatesadditionally uses the precision state of the representation of thelibrary spectrum for that library candidate.
 17. The method of claim 15,further including adding the first most likely composition of the sampleto the fourth subset of library candidates.
 18. The method of claim 15,further including adding a third watchlist of library candidates to thethird subset of library candidates.
 19. The method of claim 18, furtherincluding adding a fourth watchlist of library candidates to the fourthsubset of library candidates.
 20. The method of claim 15, wherein thedata from the sample further comprises at least one observed property ofthe sample.
 21. The method of claim 15, further including selecting thefourth subset of library candidates prior to selecting the third subsetof library candidates and adding the second most likely composition ofthe sample to the third subset of library candidates.
 22. The method ofclaim 15, further including selecting the fourth subset of librarycandidates prior to selecting the third subset of library candidates andadding the second most likely composition of the sample to the firstsubset of library candidates.
 23. A computer program product carrying anon-transitory computer program which, when executed by a process canperform a method of field analyzing chemical properties of a material,the method comprising providing a hand-held instrument comprising atleast one sensor adapted for providing Fourier transform infraredspectroscopy (FTIR) surveillance and at least another sensor forproviding Raman spectroscopy surveillance.
 24. A computer programproduct carrying a non-transitory computer program which, when executedby a process can perform a method of determining the most likelycomposition of a sample by at least two technologies using aspectrometer, the method comprising: a. obtaining data from the sampleby a first technology using the spectrometer, wherein the data comprisesa first representation of a measured spectrum obtained by the firsttechnology; b. determining a precision state of the first representationof the measured spectrum; c. providing a first set of library candidatesand, for each library candidate, providing data representing eachlibrary candidate, wherein the data comprises a representation of alibrary spectrum obtained by the first technology; d. selecting a firstsubset of library candidates by determining a first representation ofthe similarity of the sample to each library candidate in the first setof library candidates using (i) the first representation of the measuredspectrum; (ii) the precision state of the first representation of themeasured spectrum; and (iii) the representation of the library spectrumfor that library candidate; e. determining a first most likelycomposition of the sample based upon the selected first subset oflibrary candidates; f. obtaining data from the sample by a secondtechnology using the spectrometer, wherein the data comprises a secondrepresentation of a measured spectrum obtained by the second technology;g. determining a precision state of the second representation of themeasured spectrum; h. providing a second set of library candidates and,for each library candidate, providing data representing each librarycandidate, wherein the data comprises a representation of a libraryspectrum obtained by the second technology; i. selecting a second subsetof library candidates by determining a second representation of thesimilarity of the sample to each library candidate in the second set oflibrary candidates using (i) the second representation of the measuredspectrum; (ii) the precision state of the second representation of themeasured spectrum; and (iii) the representation of the library spectrumfor that library candidate; j. determining a second most likelycomposition of the sample based upon the selected second subset oflibrary candidates; k. determining a resulting most likely compositionof the sample based upon the first and second most likely composition ofthe sample; and l. displaying the most likely composition of the sampleto a user.